Algorithms for building neural networks have existed for decades. For a long time, neural networks were not widely used. Recent changes to the cost of compute and the size of our data have made neural networks extremely useful. Our smartphones generate terabytes of useful data. Lower storage costs make it economical to keep that data. Cloud computing democratized the ability to do large scale machine learning across deep learning hardware. Over the last few years, these trends have been driving ...
May 10, 2018•54 min
Keybase is a platform for managing public key infrastructure. Keybase’s products simplify the complicated process of associating your identity with a public key. Keybase is the subject of the first half of today’s show. Michael Maxim, an engineer from Keybase gives an overview of how the technology works and what kinds of applications Keybase unlocks. The second half of today’s show is about Clarifai. Clarifai is an AI platform that provides image recognition APIs as a service. Habib Talavati ex...
Apr 28, 2018•1 hr 13 min
Rajat Monga is a director of engineering at Google where he works on TensorFlow. TensorFlow is a framework for numerical computation developed at Google. The majority of TensorFlow users are building machine learning applications such as image recognition, recommendation systems, and natural language processing–but TensorFlow is actually applicable to a broader range of scientific computation than just machine learning. TensorFlow has APIs for decision trees, support vector machines, and linear ...
Apr 26, 2018•51 min
The easiest way to train a computer to recognize a picture of a cat is to show the computer a million labeled images of cats. The easiest way to train a computer to recognize a stop sign is to show the computer a million labeled stop signs. Supervised machine learning systems require labeled data. Today, most of that labeling needs to be done by humans. When a large tech company decides to “build a machine learning model,” that often requires a massive amount of effort to get labeled data. Hundr...
Feb 27, 2018•43 min
Pinterest is a visual feed of ideas, products, clothing, and recipes. Millions of users browse Pinterest to find images and text that are tailored to their interests. Like most companies, Pinterest started with a large monolithic application that served all requests. As Pinterest’s engineering resources expanded, some of the architecture was broken up into microservices and Dockerized, which make the system easier to reason about. To serve users with better feeds, Pinterest built a machine learn...
Feb 14, 2018•41 min
Training a deep learning model involves operations over tensors. A tensor is a multi-dimensional array of numbers. For several years, GPUs were used for these linear algebra calculations. That’s because graphics chips are built to efficiently process matrix operations. Tensor processing consists of linear algebra operations that are similar in some ways to graphics processing–but not identical. Deep learning workloads do not run as efficiently on these conventional GPUs as they would on speciali...
Jan 29, 2018•52 min
A modern farm has hundreds of sensors to monitor the soil health, and robotic machinery to reap the vegetables. A modern shipping yard has hundreds of computers working together to orchestrate and analyze the freight that is coming in from overseas. A modern factory has temperature gauges and smart security cameras to ensure workplace safety. All of these devices could be considered “edge” devices. Over the last decade, these edge devices have mostly been used to gather data and save it to an on...
Jan 26, 2018•51 min
Originally published November 17, 2015 “Changing anything changes everything.” Technical debt, referring to the compounding cost of changes to software architecture, can be especially challenging in machine learning systems. D. Sculley is a software engineer at Google, focusing on machine learning, data mining, and information retrieval. He recently co-authored the paper Machine Learning: The High Interest Credit Card of Technical Debt . Questions How do you define technical debt? Why does techn...
Dec 25, 2017•32 min
Automation is changing the labor market. To automate a task, someone needs to put in the work to describe the task correctly to a computer. For some tasks, the reward for automating a task is tremendous–for example, putting together mobile phones. In China, companies like FOXCONN are investing time and money into programming the instructions for how to assemble your phone. Robots execute those instructions. FOXCONN spends millions of dollars deploying these robots, but it is a worthwhile expense...
Nov 17, 2017•1 hr
Machine learning models can be built by plotting points in space and optimizing a function based off of those points. For example, I can plot every person in the United States in a 3 dimensional space: age, geographic location, and yearly salary. Then I can draw a function that minimizes the distance between my function and each of those data points. Once I define that function, you can give me your age and a geographic location, and I can predict your salary. Plotting these points in space is c...
Oct 18, 2017•41 min
A basketball game gives off endless amounts of data. Cameras from all angles capture the players making their way around the court, dribbling, passing, and shooting. With computer vision, a computer can build a well-defined understanding for what a sport looks like. With other machine learning techniques, the computer can make predictions by combining historical data with a game that is going on right now. Second Spectrum is a company that builds products for analyzing sports. At major basketbal...
Sep 29, 2017•50 min
The applications that demand deep learning range from self-driving cars to healthcare, but the way that models are developed and trained is similar. A model is trained in the cloud and deployed to a device. The device engages with the real world, gathering more data. That data is sent back to the cloud, where it can improve the model. From the processor level to the software frameworks at the top of the stack, the impact of deep learning is so significant that it is driving changes everywhere. A...
Sep 19, 2017•48 min
If I have a picture of a dog, and I want to search the Internet for pictures that look like that dog, how can I do that? I need to make an algorithm to build an index of all the pictures on the Internet. That index can define the different features of my images. I can find mathematical features in each image that describe that image. The mathematical features can be represented by a matrix of numbers. Then I can run the same algorithm on the picture of my dog, which will make another matrix of n...
Sep 15, 2017•54 min
Machines understand the world through mathematical representations. In order to train a machine learning model, we need to describe everything in terms of numbers. Images, words, and sounds are too abstract for a computer. But a series of numbers is a representation that we can all agree on, whether we are a computer or a human. In recent shows, we have explored how to train machine learning models to understand images and video. Today, we explore words. You might be thinking–”isn’t a word easy ...
Sep 13, 2017•55 min
Software companies that have been around for a decade have a ton of data. Modern machine learning techniques are able to turn that data into extremely useful models. Salesforce users have been entering petabytes of data into the company’s CRM tool since 1999. With its Einstein suite of products, Salesforce is using that data to build new product features and APIs. Simon Chan is the senior director of product management with Einstein. He oversees the efforts to give longtime Salesforce customers ...
Sep 05, 2017•50 min
Automation will make healthcare more efficient and less prone to error. Today, machine learning is already being used to diagnose diabetic retinopathy and improve radiology accuracy. Someday, an AI assistant will assist a doctor in working through a complicated differential diagnosis. Our hospitals look roughly the same today as they did ten years ago, because getting new technology into the hands of doctors and nurses is a slow process–just ask anyone who has tried to sell software in the healt...
Sep 01, 2017•44 min
Querying a search index for objects similar to a given object is a common problem. A user who has just read a great news article might want to read articles similar to it. A user who has just taken a picture of a dog might want to search for dog photos similar to it. In both of these cases, the query object is turned into a vector and compared to the vectors representing the objects in the search index. Facebook contains a lot of news articles and a lot of dog pictures. How do you index and quer...
Aug 22, 2017•53 min
Self-driving cars are here. Fully autonomous systems like Waymo are being piloted in less complex circumstances. Human-in-the-loop systems like Tesla Autopilot navigate drivers when it is safe to do so, and lets the human take control in ambiguous circumstances. Computers are great at memorization, but not yet great at reasoning. We cannot enumerate to a computer every single circumstance that a car might find itself in. The computer needs to perceive its surroundings, plan how to take action, e...
Jul 28, 2017•52 min
Instacart is a grocery delivery service. Customers log onto the website or mobile app and pick their groceries. Shoppers at the store get those groceries off the shelves. Drivers pick up the groceries and drive them to the customer. This is an infinitely complex set of logistics problems, paired with a rich data set given by the popularity of Instacart. Jeremy Stanley is the VP of data science for Instacart. In this episode, he explains how Instacart’s 4-sided marketplace business is constructed...
Jun 29, 2017•57 min
Deep learning allows engineers to build models that can make decisions based on training data. These models improve over time using stochastic gradient descent. When a model gets big enough, the training must be broken up across multiple machines. Two strategies for doing this are “model parallelism” which divides the model across machines and “data parallelism” which divides the data across multiple copies of the model. Distributed deep learning brings together two advanced software engineering...
Jun 14, 2017•52 min
Video object segmentation allows computer vision to identify objects as they move through space in a video. The DAVIS challenge is a contest among machine learning researchers working off of a shared dataset of annotated videos. The organizers of the DAVIS challenge join the show today to explain how video object segmentation models are trained and how different competitors take part in the DAVIS challenge. A good companion to this episode is our discussion of Convolutional Neural Networks with ...
Jun 05, 2017•48 min
Humans have now been defeated by computers at heads up no-limit holdem poker. Some people thought this wouldn’t be possible. Sure, we can teach a computer to beat a human at Go or Chess. Those games have a smaller decision space. There is no hidden information. There is no bluffing. Poker must be different! It is too human to be automated. The game space of poker is different than that of Go. It has 10^160 different situations–which is more than the number of atoms in the universe. And the game ...
May 12, 2017•45 min
Convolutional neural networks are a machine learning tool that uses layers of convolution and pooling to process and classify inputs. CNNs are useful for identifying objects in images and video. In this episode, we focus on the application of convolutional neural networks to image and video recognition and classification. Matt Zeiler is the CEO of Clarifai, an API for image and video recognition. Matt takes us through the basics of a convolutional neural network–you don’t need any background in ...
May 10, 2017•50 min
Most popular music today uses a computer as the central instrument. A single musician is often selecting the instruments, programming the drum loops, composing the melodies, and mixing the track to get the right overall atmosphere. With so much work to do on each song, popular musicians need to simplify–the result is that pop music today consists of simple melodies without much chord progression. Magenta is a project out of Google Brain to design algorithms that learn how to generate art and mus...
May 01, 2017•42 min
A hedge fund is a collection of investors that make bets on the future. The “hedge” refers to the fact that the investors often try to diversify their strategies so that the direction of their bets are less correlated, and they can be successful in a variety of future scenarios. Engineering-focused hedge funds have used what might be called “machine learning” for a long time to predict what will happen in the future. Numerai is a hedge fund that crowdsources its investment strategies by allowing...
Apr 03, 2017•53 min
Multiagent systems involve the interaction of autonomous agents that may be acting independently or in collaboration with each other. Examples of these systems include financial markets, robot soccer matches, and automated warehouses. Today’s guest Peter Stone is a professor of computer science who specializies in multiagent systems and robotics. In this episode, we discuss some of the canonical problems of multiagent systems, which have some overlap with the canonical problems of distributed sy...
Mar 21, 2017•41 min
Biology research is complex. The sample size of a biological data set is often too small to make confident judgments about the biological system being studied. During Jason Knight’s PhD research, the RNA sequence data that he was studying was not significant enough to make strong conclusions about the gene regulatory networks he was trying to understand. After working in academia, and then at Human Longevity, Inc Jason came to the conclusion that the best way to work towards biology breakthrough...
Mar 20, 2017•1 hr
Every company that deals with payments deals with fraud. The question is not whether fraud will occur on your system, but rather how much of it you can detect and prevent. If a payments company flags too many transactions as fraudulent, then real transactions might accidentally get flagged as well. But if you don’t reject enough of the fraudulent transactions, you might not be able to make any money at all. Because fraud detection is such a difficult optimization problem, it is a good fit for ma...
Mar 17, 2017•52 min
Machine learning frameworks like Torch and TensorFlow have made the job of a machine learning engineer much easier. But machine learning is still hard. Debugging a machine learning model is a slow, messy process. A bug in a machine learning model does not always mean a complete failure. Your model could continue to deliver usable results even in the presence of a mistaken implementation. Perhaps you made a mistake when cleaning your data, leading to an incorrectly trained model. It is a general ...
Feb 16, 2017•50 min
Deep learning uses neural networks to identify patterns. Neural networks allow us to sequence “layers” of computing, with each layer using learning algorithms such as unsupervised learning, supervised learning, and reinforcement learning. Deep learning has taken off in the last few years, but it has been around for much longer. Adam Gibson founded Skymind, the company behind Deeplearning4j. Deeplearning4j is a distributed deep learning library for Scala and Java. It integrates with Hadoop and Sp...
Feb 10, 2017•46 min
